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Published Jan 10, 2014
Ronald Mac-Ginty Astrid M Oddershede Raúl Carrasco Manuel Vargas

Abstract

This paper presents a strategic foresight study of the electrical grid throughout the South American region until 2025. The study considered the Climate Change phenomenon and many different energy sources, proposing a new methodology through the Analytic Hierarchy Process (AHP) and the Monte Carlo simulation. The study also considered the earthquake in Japan and nuclear plant accident in Fukushima, and the technological convergence that will occur over the next 15 years in the electric grid sources. The research involved political, economic, social and technological (PEST) factors. Through PEST analysis and the involvement of an expert panel, it was possible to select the most influential variable for each PEST factor. In order to prioritize these factors and evaluate the different technological alternatives, an AHP model was developed. Then a Monte Carlo simulation was run 1000 times for electric generator source clusters. Four prospective scenarios of the electrical grid structure until 2025 in the South American region were defined. The study highlighted the contribution of renewable energy adding nuclear power as the main mix group as a source of energy by 2025. This indicates that it is possible to anticipate an electric grid until 2025 in the South American region with low impact on Climate Change.


http://dx.doi.org/10.13033/ijahp.v5i2.195

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Keywords

Climate Change, EnergySources, AHP, PEST Analysis, Monte Carlo Method

References
Alford, K., Keenihan, S., & McGrail, S. (2012). The complex futures of emerging
technologies: challenges and opportunities for science foresight and governance in
Australia. Journal of Futures Studies, 16(4), 67–86.
Canton, J. (2006). The extreme future: The top trends that will reshape the world for the
next 5, 10, and 20 years. New York: Dutton.
Chatzimouratidis, A. I., & Pilavachi, P. A. (2008). Multicriteria evaluation of power
plants impact on the living standard using the analytic hierarchy process. Energy policy,
36(3), 1074–1089.
Chatzimouratidis, A. I., & Pilavachi, P. A. (2009). Technological, economic and
sustainability evaluation of power plants using the Analytic Hierarchy Process. Energy
Policy, 37(3), 778–787.
Edenhofer, O., Pichs-Madruga, R., & Sokona, Y. (2011). Renewable energy sources and
climate change mitigation: special report of the Intergovernmental Panel on Climate
Change. Cambridge, UK: Cambridge University Press.
Emblemsvåg, J., & Tonning, L. (2003). Decision support in selecting maintenance
organization. Journal of Quality in Maintenance Engineering, 9(1), 11–24.
doi:10.1108/13552510310466765
Glenn, J. C., & Gordon, T. J. (2009). Futures research methodology: The Millennium
Project: Version 3.0. Washington, D.C.: Milennium Project.
Godet, M. (2000). The art of scenarios and strategic planning: tools and pitfalls.
Technological forecasting and social change, 65(1), 3–22.
Godet, M., i Buisán, E. P., & Posiello, J. G. (1995). De la anticipación a la acción:
Manual de prospectiva y estrategia. Alfaomega México.
Godet, M., & MONTI, R. R. (2000). La caja de herramientas de la prospectiva
estratégica. Gerpa.
Kaku, M. (2011). Physics of the future: How science will shape human destiny and our
daily lives by the year 2100. Knopf Doubleday Publishing Group.
Kurzweil, R. (2005). The singularity is near: When humans transcend biology. New
York: Viking Penguin.
Laguna, I. (2002). Generación de energía eléctrica y medio ambiente. Gaceta Ecológica,
(65), 53–62.
Liu, G., Rasul, M. G., Amanullah, M. T. O., & Khan, M. M. K. (2010). AHP and fuzzy
assessment based sustainability indicator for hybrid renewable energy system. In
Universities Power Engineering Conference (AUPEC), 2010 20th Australasian (pp. 1–6).
Meier, P. J. (2002). Life-cycle assessment of electricity generation systems and
applications for climate change policy analysis. (Unpublished doctoral dissertation).
University of Wisconsin, Madison.
Momani, A. M., & Ahmed, A. A. (2011). Material handling equipment selection using
hybrid Monte Carlo simulation and Analytic Hierarchy Process, World Academy of
Science, Engineering and Technology, 59, 953–958.
Monroy, I. L. (2002). La generación de energía eléctrica y el ambiente. Gaceta
Ecológica, (065), 53–62.
National nanotechnology Initiative. (2012).
Saaty, T. L. (2001). Decision making with dependence and feedback: The Analytic
Network Process (2 ed.), Pittsburgh: RWS Publications.
Saaty, T. L. (2007). The analytic hierarchy and analytic network measurement processes:
Applications to decisions under risk. European Journal of Pure and Applied
Mathematics, 1(1), 122–196.
Vecchiato, R. (2012). Environmental uncertainty, foresight and strategic decision
making: An integrated study. Technological Forecasting and Social Change, 79(3), 436–
447. doi:10.1016/j.techfore.2011.07.010
Weinberger, S. (2012). Laser plant offers cheap way to make nuclear fuel. Nature,
487(7405), 16–17.
World Nuclear Association Report. (2010). Comparison of Lifecycle Greenhouse Gas
Emissions of Various Electricity Generation Sources.
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